JPMorgan Lead Data Scientist 

United States, Ohio 

192023160

Job responsibilities:

• Work closely with Cyber Technology Group's subject matter experts (SMEs) to align data science initiatives with cybersecurity objectives.
• Extract, analyze, and interpret data from JPMC data sources, assessing the effectiveness and precision of new data sources and data collection techniques. Leverage big data technologies to handle and process large-scale datasets efficiently.
• Develop bespoke data models and algorithms tailored to meet the specific needs of the Cyber Technology Group's requirements and apply them to data sets while developing and employing the company's A/B testing framework to assess model quality.
• Apply advanced principles, theories, and concepts in the realm of Artificial Intelligence (AI), Machine Learning (ML), Large Language Models (LLMs), Deep Learning (DL), Generative AI, Transfer Learning, and Reinforcement Learning algorithms to cyber data sets.
• Coordinate with various functional teams to implement models and track results
• Establish robust processes and tools for monitoring model performance using industry standard metrics and data accuracy, ensuring continuous improvement. Assess and select suitable Large Language Model (LLM) tools and models for diverse tasks, focusing on parameter-efficient, mixture-of-expert, and instruction methods.
• Curate custom datasets and fine-tune LLMs to enhance their performance and applicability.
• Design and develop advanced LLM prompts, Retrieval-Augmented Generation (RAG) solutions, and intelligent agents for LLMs. Conduct experiments to push the capability limits of LLM models and enhance their dependability.
• Orchestrate multiple models and develop innovative approaches for handling sparse-data situations, and develop custom models when suitable models are unavailable in JPMC's inventory, from suppliers, or in the open-source domain.
• Leverage LLM APIs to automate routine and complex cyber operational tasks, enhancing efficiency and response times, and integrate LLM-driven automation into existing cybersecurity frameworks to streamline operations and reduce manual intervention.
• Continuously evaluate and refine LLM API usage to ensure alignment with evolving cyber threats and operational needs.

Required qualifications, capabilities, and skills:

• Bachelors or Master's degree with at least 5 years of experience in computer science, statistics, artificial intelligence, machine learning, or a related field with hands-on experience in designing and developing end-to-end AI solutions.
• Experience in developing ML pipelines, including data gathering, preparation, model selection, training, testing, validation, and prediction.
• Proficiency in a wide range of algorithms and models, including Linear Regression, Decision Trees, Neural Networks, and LLMs more using python and other AI/ML programming languages and frameworks.
• Experience in backend and frontend development, including databases, programming languages, web frameworks, and APIs.
• Strong understanding of statistical theory, data mining, and machine learning algorithms.
• Expertise in Python, SQL, and Spark for developing large-scale applications.
• Strong problem-solving skills with a focus on product development and proven experience in applying AI to practical technology solutions.
• Experience in data architecture and machine learning techniques.
• Excellent written and verbal communication skills for team coordination.
• Eagerness to learn and excel at new technologies and techniques.

Preferred qualifications, capabilities, and skills:

• Experience in effectively utilizing GPU and compute resources in frameworks like PyTorch or TensorFlow.
• Proficiency in multiple programming languages, including C, C++, and object-oriented languages.
• Extensive experience in cloud environments such as AWS, Google Cloud, and Azure, with a focus on deploying and managing AI/ML solutions.
• Familiarity with CI/CD pipelines and Agile methodology.
• Knowledge of statistical and data mining techniques, including GLM/Regression, Random Forest, and more.
• Experience with web services and distributed data/computing tools, such as Hadoop, Hive and Spark.